Systems and methods for generating a plain english interpretation of a legal clause

ABSTRACT

A system is configured to perform one or more steps of a method. The system may receive a plurality of attorney communications, identify one or more legal clause interpretations in them, receive a first legal clause and provide it to a trained NN and a probability model. The system may also generate a corresponding first plain English interpretation based on the first legal clause, provide the first plain English interpretation to the probability model, which generates a probability score based on a degree to which the legal clause matches the plain English interpretation in meaning, and determine whether the probability score exceeds a predetermined threshold. Further, the system may instruct the NN to generate a second plain English interpretation based on the first legal clause when the probability score does not exceed the predetermined threshold, and output the first plain English interpretation when the probability score exceeds the predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, and claims priority under 35U.S.C. § 120 to, U.S. application Ser. No. 16/515,650, filed Jul. 18,2019, which is a continuation of U.S. patent application Ser. No.16/273,964, filed Feb. 12, 2019, now U.S. Pat. No. 10,452,784, whichclaims the benefit of U.S. Provisional Application No. 62/776,941 filedDec. 7, 2018, the entire contents and substance of which are herebyincorporated by reference.

FIELD OF INVENTION

The present disclosure relates to systems and methods for generating aplain English interpretation of a legal clause, and more particularly tosystems and methods using a neural network (NN) to generate a plainEnglish interpretation of a legal clause.

BACKGROUND

Legal documents tend to be difficult to read and understand, often dueto the presence archaic “legalese” jargon or terms. As a result, it canbe hard for involved parties to understand the implications of variousterms or clauses included in their documents or agreements. Further,even lawyers who draft such legal documents may have difficulties inunderstanding and/or forecasting the future effects of such clauses.This analysis is even further complicated by the fact that legalspecific terms or clauses could have different implications depending onthe location (e.g., jurisdiction) in which they are used. Even for thosewho can understand complex legal documents, analyzing the documents cantake considerable time and, in turn, expense.

Accordingly, there is a need for systems and methods for providing aconcise plain English version of a legal clause of a legal document.Embodiments of the present disclosure are directed to this and otherconsiderations.

SUMMARY

Disclosed embodiments provide systems and methods using a NN forgenerating a plain English interpretation of a legal clause.

Consistent with the disclosed embodiments, various methods and systemsare disclosed. In an embodiment, a method for generating a plain Englishinterpretation of a legal clause is disclosed. The method may beimplemented with a computing device. The method may include receiving aplurality of attorney communications. The method may also includeidentifying one or more legal clause interpretations in the plurality ofattorney communications. The method may also include training a neuralnetwork (NN) based on the identified one or more legal clauseinterpretations. The method may include receiving a first legal clause.The method may include providing the legal clause to the trained N and aprobability model. The method may include generating, via the trainedNN, a corresponding first plain English interpretation based on thefirst legal clause. The method may include providing the first plainEnglish interpretation to a probability model, which generates aprobability score based on a degree to which the legal clause matchesthe plain English interpretation in meaning. The method also includesdetermining whether the probability score exceeds a predeterminedthreshold. The method also includes instructing the NN to generate asecond plain English interpretation based on the first legal clause whenthe probability score does not exceed the predetermined threshold.Finally, the method includes outputting the first plain Englishinterpretation when the probability score exceeds the predeterminedthreshold.

Further features of the disclosed design, and the advantages offeredthereby, are explained in greater detail hereinafter with reference tospecific embodiments illustrated in the accompanying drawings, whereinlike elements are indicated be like reference designators.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and which are incorporated into andconstitute a portion of this disclosure, illustrate variousimplementations and aspects of the disclosed technology and, togetherwith the description, serve to explain the principles of the disclosedtechnology. In the drawings:

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure;

FIG. 2 is a component diagram of a service provider terminal accordingto an example embodiment;

FIG. 3 is a component diagram of a computing device according to anexample embodiment;

FIG. 4A and FIG. 4B are flowcharts of a method for training a neuralnetwork to generate a plain English interpretation of a legal clause andgenerating a plain English interpretation of a legal clause according toan example embodiment;

FIG. 5 is a flow chart of a method for training a neural network togenerate a plain English interpretation of a legal claims and generatinga plain English interpretation of a legal clause according to an exampleembodiment; and

FIG. 6A and FIG. 6B are flowcharts of a method for generating a plainEnglish interpretation of a legal clause according to an exampleembodiment.

DETAILED DESCRIPTION

Some implementations of the disclosed technology will be described morefully with reference to the accompanying drawings. This disclosedtechnology may, however, be embodied in many different forms and shouldnot be construed as limited to the implementations set forth herein. Thecomponents described hereinafter as making up various elements of thedisclosed technology are intended to be illustrative and notrestrictive. Many suitable components that would perform the same orsimilar functions as components described herein are intended to beembraced within the scope of the disclosed electronic devices andmethods. Such other components not described herein may include, but arenot limited to, for example, components developed after development ofthe disclosed technology.

It is also to be understood that the mention of one or more method stepsdoes not preclude the presence of additional method steps or interveningmethod steps between those steps expressly identified. Similarly, it isalso to be understood that the mention of one or more components in adevice or system does not preclude the presence of additional componentsor intervening components between those components expressly identified.

As used herein, the term “legalese” refers to the specialized languageof the legal profession. The goal of this disclosure is to generateaccurate plain English interpretations of legal clauses.

This disclosure discusses using a neural network (NN) to translate froma legalese to plain English. It is envisioned that the NN could be arecurrent neural network (RNN), a convolutional neural network (CNN), ora recurrent convolutional neural network (RCNN).

The RNN takes in characters, words, or sentences one at a time. Each ofthe characters, words, or sentences are fed into the RNN one afteranother. The RNN has cells (e.g., long short-term memory units) thathave can remember prior characters, words, or sentences. In contrast, aCNN takes in all characters, words, or sentences at once making CNNsfaster at computing than RNNs. Thus, CNN may be better at translating aparagraph to a sentence. However, the CNN cannot remember what happenedbefore the paragraph since it takes all of the characters, words, orsentences in at once. The RCNN is some combination of a RNN and a CNN.Typically, the RNN will accept the output of the CNN in the RCNN.

The present disclosure relates to methods and systems for using a neuralnetwork, and, in particular, for utilizing the NN to generate a plainEnglish interpretation of a legal clause. In some embodiments, a methodmay include receiving a plurality of attorney communications. The methodmay also include identifying one or more legal clause interpretations inthe plurality of attorney communications. The method may also includetraining a neural network (NN) based on the identified one or more legalclause interpretations. The method may include receiving a first legalclause. The method may include providing the legal clause to the trainedN and a probability model. The method may include generating, via thetrained NN, a corresponding first plain English interpretation based onthe first legal clause. The method may include providing the first plainEnglish interpretation to a probability model, which generates aprobability score based on a degree to which the legal clause matchesthe plain English interpretation in meaning. The method also includesdetermining whether the probability score exceeds a predeterminedthreshold. The method also includes instructing the NN to generate asecond plain English interpretation based on the first legal clause whenthe probability score does not exceed the predetermined threshold.Finally, the method includes outputting the first plain Englishinterpretation when the probability score exceeds the predeterminedthreshold.

Reference will now be made in detail to example embodiments of thedisclosed technology, examples of which are illustrated in theaccompanying drawings and disclosed herein. Wherever convenient, thesame references numbers will be used throughout the drawings to refer tothe same or like parts.

FIG. 1 is a diagram of an example system environment that may be used toimplement one or more embodiments of the present disclosure. Thecomponents and arrangements shown in FIG. 1 are not intended to limitthe disclosed embodiments as the components used to implement thedisclosed processes and features may vary.

In accordance with disclosed embodiments, system 100 may include aservice provider system 110 in communication with a computing device 120via network 105. In some embodiments, service provider system 110 mayalso be in communication with various databases. Computing device 120may be a mobile computing device (e.g., a smart phone, tablet computer,smart wearable device, portable laptop computer, voice command device,wearable augmented reality device, or other mobile computing device) ora stationary device (e.g., desktop computer).

In some embodiments, the computing device 120 may transmit a legalclause of a legal document (or an entire legal document) to the serviceprovider system 110, and the service provider system 110 may utilize aNN to translate the legal clause or legal document into plain English.In some embodiments, the server provider terminal 110 may control thecomputing device 120 to implement one or more aspects of the NN. In somecases, the computing device 120 may perform pre-processing on the legalclause (or legal document) before sending pre-processed legal clause (orlegal document) to the service provider system 110. For example, thecomputing device 120 may perform an optical character recognition scanon a legal document containing one or more legal clauses. A user, usingthe computing device 120, may select a legal clause from the legaldocument for translation into plain English. As another example, thecomputing device 120 may normalize a legal clause by e.g., convertingnumbers to words.

Network 105 may be of any suitable type, including individualconnections via the internet such as cellular or WiFi networks. In someembodiments, network 105 may connect terminals using direct connectionssuch as radio-frequency identification (RFID), near-field communication(NFC), Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols, USB, or LAN. Because theinformation transmitted may be personal or confidential, securityconcerns may dictate one or more of these types of connections beencrypted or otherwise secured. In some embodiments, however, theinformation being transmitted may be less personal, and therefore thenetwork connections may be selected for convenience over security.

An example embodiment of service provider system 110 is shown in moredetail in FIG. 2. Computing device 120 may have a similar structure andcomponents that are similar to those described with respect to serviceprovider system 110. As shown, service provider system 110 may include aprocessor 210, an input/output (“I/O”) device 220, a memory 230containing an operating system (“OS”) 240 and a program 250. Forexample, service provider system 110 may be a single server or may beconfigured as a distributed computer system including multiple serversor computers that interoperate to perform one or more of the processesand functionalities associated with the disclosed embodiments. In someembodiments, service provider system 110 may further include aperipheral interface, a transceiver, a mobile network interface incommunication with processor 210, a bus configured to facilitatecommunication between the various components of the service providersystem 110, and a power source configured to power one or morecomponents of service provider system 110.

A peripheral interface may include the hardware, firmware and/orsoftware that enables communication with various peripheral devices,such as media drives (e.g., magnetic disk, solid state, or optical diskdrives), other processing devices, or any other input source used inconnection with the instant techniques. In some embodiments, aperipheral interface may include a serial port, a parallel port, ageneral-purpose input and output (GPIO) port, a game port, a universalserial bus (USB), a micro-USB port, a high definition multimedia (HDMI)port, a video port, an audio port, a Bluetooth™ port, a near-fieldcommunication (NFC) port, another like communication interface, or anycombination thereof.

In some embodiments, a transceiver may be configured to communicate withcompatible devices and ID tags when they are within a predeterminedrange. A transceiver may be compatible with one or more of:radio-frequency identification (RFID), near-field communication (NFC),Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™ ambientbackscatter communications (ABC) protocols or similar technologies.

A mobile network interface may provide access to a cellular network, theInternet, or another wide-area network. In some embodiments, a mobilenetwork interface may include hardware, firmware, and/or software thatallows processor(s) 210 to communicate with other devices via wired orwireless networks, whether local or wide area, private or public, asknown in the art. A power source may be configured to provide anappropriate alternating current (AC) or direct current (DC) to powercomponents.

As described above, service provider system 110 may configured toremotely communicate with one or more other devices, such as computerdevice 120. According to some embodiments, service provider system 110may utilize a NN to translate one or more legal clauses from legalese toplain English.

Processor 210 may include one or more of a microprocessor,microcontroller, digital signal processor, co-processor or the like orcombinations thereof capable of executing stored instructions andoperating upon stored data. Memory 230 may include, in someimplementations, one or more suitable types of memory (e.g. such asvolatile or non-volatile memory, random access memory (RAM), read onlymemory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), magnetic disks, optical disks,floppy disks, hard disks, removable cartridges, flash memory, aredundant array of independent disks (RAID), and the like), for storingfiles including an operating system, application programs (including,for example, a web browser application, a widget or gadget engine, andor other applications, as necessary), executable instructions and data.In one embodiment, the processing techniques described herein areimplemented as a combination of executable instructions and data withinthe memory 230.

Processor 210 may be one or more known processing devices, such as amicroprocessor from the Pentium™ family manufactured by Intel™ or theTurion™ family manufactured by AMD™. Processor 210 may constitute asingle core or multiple core processor that executes parallel processessimultaneously. For example, processor 210 may be a single coreprocessor that is configured with virtual processing technologies. Incertain embodiments, processor 210 may use logical processors tosimultaneously execute and control multiple processes. Processor 210 mayimplement virtual machine technologies, or other similar knowntechnologies to provide the ability to execute, control, run,manipulate, store, etc. multiple software processes, applications,programs, etc. One of ordinary skill in the art would understand thatother types of processor arrangements could be implemented that providefor the capabilities disclosed herein.

Service provider system 110 may include one or more storage devicesconfigured to store information used by processor 210 (or othercomponents) to perform certain functions related to the disclosedembodiments. In one example, service provider system 110 may includememory 230 that includes instructions to enable processor 210 to executeone or more applications, such as server applications, networkcommunication processes, and any other type of application or softwareknown to be available on computer systems. Alternatively, theinstructions, application programs, etc. may be stored in an externalstorage or available from a memory over a network. The one or morestorage devices may be a volatile or non-volatile, magnetic,semiconductor, tape, optical, removable, non-removable, or other type ofstorage device or tangible computer-readable medium.

In one embodiment, service provider system 110 may include memory 230that includes instructions that, when executed by processor 210, performone or more processes consistent with the functionalities disclosedherein. Methods, systems, and articles of manufacture consistent withdisclosed embodiments are not limited to separate programs or computersconfigured to perform dedicated tasks. For example, service providersystem 110 may include memory 230 that may include one or more programs250 to perform one or more functions of the disclosed embodiments.Moreover, processor 210 may execute one or more programs 250 locatedremotely from service provider system 110. For example, service providersystem 110 may access one or more remote programs 250, that, whenexecuted, perform functions related to disclosed embodiments.

Memory 230 may include one or more memory devices that store data andinstructions used to perform one or more features of the disclosedembodiments. Memory 230 may also include any combination of one or moredatabases controlled by memory controller devices (e.g., server(s),etc.) or software, such as document management systems, Microsoft™ SQLdatabases, SharePoint™ databases, Oracle™ databases, Sybase™ databases,or other relational databases. Memory 230 may include softwarecomponents that, when executed by processor 210, perform one or moreprocesses consistent with the disclosed embodiments. In someembodiments, memory 230 may include an image processing database 260 anda neural-network pipeline database 270 for storing related data toenable service provider system 110 to perform one or more of theprocesses and functionalities associated with the disclosed embodiments.

Service provider system 110 may also be communicatively connected to oneor more memory devices (e.g., databases (not shown)) locally or througha network. The remote memory devices may be configured to storeinformation and may be accessed and/or managed by service providersystem 110. By way of example, the remote memory devices may be documentmanagement systems, Microsoft™ SQL database, SharePoint™ databases,Oracle™ databases, Sybase™ databases, or other relational databases.Systems and methods consistent with disclosed embodiments, however, arenot limited to separate databases or even to the use of a database.

Service provider system 110 may also include one or more I/O devices 220that may include one or more interfaces for receiving signals or inputfrom devices and providing signals or output to one or more devices thatallow data to be received and/or transmitted by service provider system110. For example, service provider system 110 may include interfacecomponents, which may provide interfaces to one or more input devices,such as one or more keyboards, mouse devices, touch screens, track pads,trackballs, scroll wheels, digital cameras, microphones, sensors, andthe like, that enable service provider system 110 to receive data fromone or more users (such as via computing device 120).

In example embodiments of the disclosed technology, service providersystem 110 may include any number of hardware and/or softwareapplications that are executed to facilitate any of the operations. Theone or more I/O interfaces may be utilized to receive or collect dataand/or user instructions from a wide variety of input devices. Receiveddata may be processed by one or more computer processors as desired invarious implementations of the disclosed technology and/or stored in oneor more memory devices.

While service provider system 110 has been described as one form forimplementing the techniques described herein, those having ordinaryskill in the art will appreciate that other, functionally equivalenttechniques may be employed. For example, as known in the art, some orall of the functionality implemented via executable instructions mayalso be implemented using firmware and/or hardware devices such asapplication specific integrated circuits (ASICs), programmable logicarrays, state machines, etc. Furthermore, other implementations of theterminal 110 may include a greater or lesser number of components thanthose illustrated.

FIG. 3 shows an example embodiment of computing device 120. As shown,computing device 120 may include input/output (“I/O”) device 220 forreceiving data from another device (e.g., service provider system 110),memory 230 containing operating system (“OS”) 240, program 250, and anyother associated component as described above with respect to serviceprovider system 110. Computing device 120 may also have one or moreprocessors 210, a geographic location sensor (“GLS”) 304 for determiningthe geographic location of computing device 120, a display 306 fordisplaying content such as text messages, images, and selectablebuttons/icons/links, an environmental data (“ED”) sensor 308 forobtaining environmental data including audio and/or visual information,and a user interface (“U/I”) device 310 for receiving user input data,such as data representative of a click, a scroll, a tap, a press, ortyping on an input device that can detect tactile inputs. User inputdata may also be non-tactile inputs that may be otherwise detected by EDsensor 308. For example, user input data may include auditory commands.According to some embodiments, U/I device 310 may include some or all ofthe components described with respect to input/output device 220 above.In some embodiments, environmental data sensor 308 may include amicrophone and/or an image capture device, such as a digital camera.

FIG. 4A and FIG. 4B are flowcharts of a method 400A and 400B,respectively, for training a neural network to generate a plain Englishinterpretation of a legal clause and generating a plain Englishinterpretation of the legal clause. Methods 400A and 400B may beperformed by one or more of the service provider system 110 and thecomputing device 120 of the system 100.

In block 402, the system may receive a plurality of attorneycommunications. The plurality of attorney communications may be emailsbetween the attorney and a client. The emails may include attachments oflegal documents such as a nondisclosure agreement, a draft patentapplication, an assignment, an employment agreement, etc.

In block 404, the system may identify one or more legal clauseinterpretations in the plurality of attorney communications. Asdiscussed above, the plurality of attorney communications may be emailcommunications. The system may detect a redline change (i.e., acorrection or modification to document typically with insertions beingunderlined and deletions being strikethrough) in a document attached toone of the plurality of email communications. The redline changecontains an edit to a legal document that may change a legal clause'smeaning. For example, when an attorney removes text in a clause, therevised text (i.e., the original text minus the removed text) is ageneration of a plain English interpretation of the legal clause, whichthe system may identify the paragraph associated with the redline changeas a legal clause interpretation. The system may detect an addition in adocument attached to one of the plurality of email communications. Thesystem may identify a paragraph associated with the addition as a plainEnglish interpretation of the original paragraph. The system may detecta comment in a document attached to one of the plurality of emailcommunications and identify text within the comment as a legal clauseinterpretation of the associated paragraph.

In block 406, the system may train a NN based on the identified one ormore legal clause interpretations. For example, the system may feed theidentified one or more legal clause interpretations along with theoriginal text of the legal clause to the NN. As discussed previously theneural network may be a RNN, a CNN, or a RCNN.

In block 408, the system may receive a legal clause. According to someembodiments, the service provider system 110 receives one or more legalclauses or an entire legal document. In other embodiments, the legalclause is received and then recognized as a legal clause suitable fortranslation rather than a non-legal clause (e.g., a clause from atechnical report). In some embodiments, the method may include receivinga document rather than receiving a legal clause. The method may furtherinclude the step of identifying a legal clause in the received document.The step of identifying may be performed by a RNN using long short-termmemory (LSTM) units or a CNN.

In block 410, the system may provide the legal clause to the trained NNand a probability model.

In block 412, the system may generate, via the trained NN acorresponding first plain English interpretation based on the firstlegal clause. In some cases, the service provider system 110 performsthe translation. In other cases, the computing device 120 performstranslation.

In block 414, the system may provide the first plain Englishinterpretation to the probability model.

In block 416, the system may generate, using the probability model aprobability score based on a degree to which the legal clause matchesthe plain English interpretation in meaning. The score may be any numberfrom 0 to 100, with 100 being a complete match in meaning and 0 being nomatch in meaning.

In block 418, the system may determine whether the probability scoreexceeds a predetermined threshold. For example, the system may have apredetermined threshold score of 70. If the system determines that theprobability score does not exceed the predetermined threshold (i.e.,determination block 418=No), then in block 420, the system may instructthe NN to generate a second plain English interpretation based on thefirst legal clause. If the system determines that the probability scoreexceeds the predetermined threshold (i.e., determination block 418=Yes),then in block 422, the system may output the first plain Englishinterpretation. For example, the service provider system 110 may emailthe user with a translated version of the legal clause. As anotherexample, the service provider system 110 may provide to the user theplain English version of the legal clause via a website by displayingthe results of the translation on the website accessible by thecomputing device 120. As further example, the service provider system110 may provide the first plain English interpretation in a chat window.

FIG. 5 is a flowchart of a method 500 for training a neural network togenerate a plain English interpretation of a legal clause and generatinga plain English interpretation of the legal clause. Method 500 may beperformed by one or more of the service provider system 110 and thecomputing device 120 of the system 100.

In method 500, blocks 502, 504, 506, 508, 510, and 512 may be the sameas or similar to the steps as blocks 402, 404, 406, 408, 410, and 412,respectively, thus their descriptions will not be repeated for brevity.In block 530, the system may output the first plain Englishinterpretation. For example, the service provider system 110 may emailthe user with a translated version of the legal clause. As anotherexample, the service provider system 110 may provide to the user theplain English version of the legal clause via a website by displayingthe results of the translation on the website accessible by thecomputing device 120. As further example, the service provider system110 may provide the first plain English interpretation in a chat window.

FIG. 6A and FIG. 6B are flowcharts of a method 600A and 600B,respectively, for generating a plain English interpretation of the legalclause. Methods 600A and 600B may be performed by one or more of theservice provider system 110 and the computing device 120 of the system100.

In method 600A, blocks 608, 610, 612, 614, and 616 may be the same as orsimilar to blocks 408, 410, 412, 414, and 416, respectively, thus theirdescriptions will not be repeated for brevity. In method 600B, blocks618, 620, and 622 are the same steps as blocks 418, 420, and 422, thustheir descriptions will not be repeated for brevity.

Certain implementations provide the advantage of translating a legalclause from legalese to plain English. Thus, making legal clauses andlegal documents more understandable.

As used in this application, the terms “component,” “module,” “system,”“server,” “processor,” “memory,” and the like are intended to includeone or more computer-related units, such as but not limited to hardware,firmware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited tobeing, a process running on a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a computing device and thecomputing device can be a component. One or more components can residewithin a process and/or thread of execution and a component may belocalized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate by way of local and/or remote processessuch as in accordance with a signal having one or more data packets,such as data from one component interacting with another component in alocal system, distributed system, and/or across a network such as theInternet with other systems by way of the signal.

Certain embodiments and implementations of the disclosed technology aredescribed above with reference to block and flow diagrams of systems andmethods and/or computer program products according to exampleembodiments or implementations of the disclosed technology. It will beunderstood that one or more blocks of the block diagrams and flowdiagrams, and combinations of blocks in the block diagrams and flowdiagrams, respectively, can be implemented by computer-executableprogram instructions. Likewise, some blocks of the block diagrams andflow diagrams may not necessarily need to be performed in the orderpresented, may be repeated, or may not necessarily need to be performedat all, according to some embodiments or implementations of thedisclosed technology.

These computer-executable program instructions may be loaded onto ageneral-purpose computer, a special-purpose computer, a processor, orother programmable data processing apparatus to produce a particularmachine, such that the instructions that execute on the computer,processor, or other programmable data processing apparatus create meansfor implementing one or more functions specified in the flow diagramblock or blocks. These computer program instructions may also be storedin a computer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement one or more functions specified in the flow diagram blockor blocks.

As an example, embodiments or implementations of the disclosedtechnology may provide for a computer program product, including acomputer-usable medium having a computer-readable program code orprogram instructions embodied therein, said computer-readable programcode adapted to be executed to implement one or more functions specifiedin the flow diagram block or blocks. Likewise, the computer programinstructions may be loaded onto a computer or other programmable dataprocessing apparatus to cause a series of operational elements or stepsto be performed on the computer or other programmable apparatus toproduce a computer-implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide elementsor steps for implementing the functions specified in the flow diagramblock or blocks.

Accordingly, blocks of the block diagrams and flow diagrams supportcombinations of means for performing the specified functions,combinations of elements or steps for performing the specifiedfunctions, and program instruction means for performing the specifiedfunctions. It will also be understood that each block of the blockdiagrams and flow diagrams, and combinations of blocks in the blockdiagrams and flow diagrams, can be implemented by special-purpose,hardware-based computer systems that perform the specified functions,elements or steps, or combinations of special-purpose hardware andcomputer instructions.

Certain implementations of the disclosed technology are described abovewith reference to user devices may include mobile computing devices.Those skilled in the art recognize that there are several categories ofmobile devices, generally known as portable computing devices that canrun on batteries but are not usually classified as laptops. For example,mobile devices can include, but are not limited to portable computers,tablet PCs, internet tablets, PDAs, ultra-mobile PCs (UMPCs), wearabledevices, and smart phones. Additionally, implementations of thedisclosed technology can be utilized with internet of things (IoT)devices, smart televisions and media devices, appliances, automobiles,toys, and voice command devices, along with peripherals that interfacewith these devices.

In this description, numerous specific details have been set forth. Itis to be understood, however, that implementations of the disclosedtechnology may be practiced without these specific details. In otherinstances, well-known methods, structures and techniques have not beenshown in detail in order not to obscure an understanding of thisdescription. References to “one embodiment,” “an embodiment,” “someembodiments,” “example embodiment,” “various embodiments,” “oneimplementation,” “an implementation,” “example implementation,” “variousimplementations,” “some implementations,” etc., indicate that theimplementation(s) of the disclosed technology so described may include aparticular feature, structure, or characteristic, but not everyimplementation necessarily includes the particular feature, structure,or characteristic. Further, repeated use of the phrase “in oneimplementation” does not necessarily refer to the same implementation,although it may.

Throughout the specification and the claims, the following terms take atleast the meanings explicitly associated herein, unless the contextclearly dictates otherwise. The term “connected” means that onefunction, feature, structure, or characteristic is directly joined to orin communication with another function, feature, structure, orcharacteristic. The term “coupled” means that one function, feature,structure, or characteristic is directly or indirectly joined to or incommunication with another function, feature, structure, orcharacteristic. The term “or” is intended to mean an inclusive “or.”Further, the terms “a,” “an,” and “the” are intended to mean one or moreunless specified otherwise or clear from the context to be directed to asingular form. By “comprising” or “containing” or “including” is meantthat at least the named element, or method step is present in article ormethod, but does not exclude the presence of other elements or methodsteps, even if the other such elements or method steps have the samefunction as what is named.

As used herein, unless otherwise specified the use of the ordinaladjectives “first,” “second,” “third,” etc., to describe a commonobject, merely indicate that different instances of like objects arebeing referred to, and are not intended to imply that the objects sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

While certain embodiments of this disclosure have been described inconnection with what is presently considered to be the most practicaland various embodiments, it is to be understood that this disclosure isnot to be limited to the disclosed embodiments, but on the contrary, isintended to cover various modifications and equivalent arrangementsincluded within the scope of the appended claims. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

This written description uses examples to disclose certain embodimentsof the technology and also to enable any person skilled in the art topractice certain embodiments of this technology, including making andusing any apparatuses or systems and performing any incorporatedmethods. The patentable scope of certain embodiments of the technologyis defined in the claims, and may include other examples that occur tothose skilled in the art. Such other examples are intended to be withinthe scope of the claims if they have structural elements that do notdiffer from the literal language of the claims, or if they includeequivalent structural elements with insubstantial differences from theliteral language of the claims.

Example Use Case

The following example use case describes an example of a typical use oftraining a NN generating a plain English interpretation based on a legalclause. It is intended solely for explanatory purposes and not inlimitation. In one example, a service provider system 110 receives aplurality of attorney emails. The service provider system 110 identifiesone or more legal clause interpretations in the plurality of attorneycommunications detecting a comment in a document attached to one of theattorney emails and identifying the text within the comment as a legalclause interpretation. The service provider system 110 then trains a RNNbased on the identified one or more legal interpretations. The serviceprovider system 110 receives a legal clause, such as a non-competeclause from an employment contract. The service provider system 110provides the legal clause to the trained RNN and a probability model.The service provider system 110 generates, via the trained NN, acorresponding first plain English interpretation based on the legalclause. The service provider system 110 provides the first plain Englishinterpretation to the probability module. The service provider system110 generates, using the probability model, a probability score based ona degree to which the legal clause matches the plain Englishinterpretation in meaning. The service provider system 110 determineswhether the probability score exceeds a predetermined threshold. Forexample, the service provider system 110 have a predetermined thresholdof 70. The service provider system 110 may instruct the RNN to generatea second plain English interpretation based on the first legal clausewhen the probability score does not exceed the predetermined threshold.For example, the services provider system 110 may determine that theplain English interpretation of non-compete clause with a score of 60does not exceed the predetermined threshold of 70, thus, the serviceprovider system 110 may provide the same legal clause back the RNN forprocessing. The service provider system 110 may output the plain Englishtranslation to the user via a website display, email, or a chat windowwhen the service provider system 110 determines that the probabilityscore exceed the predetermined threshold.

What is claimed is:
 1. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: identify one or more legal clause interpretations; train a neural network (NN) based on the identified one or more legal clause interpretations; generate, via the trained NN, a first interpretation based on a first legal clause; generate, using the probability model, a probability score based on a degree to which the first legal clause matches the first interpretation in meaning; determine whether the probability score exceeds a predetermined threshold; when the probability score does not exceed the predetermined threshold, instruct the NN to generate a second interpretation based on the first legal clause; and when the probability score exceeds the predetermined threshold, output the first interpretation.
 2. The system of claim 1, wherein the probability model is a convolutional neural network (CNN) and the NN is either a CNN or a recurrent neural network (RNN).
 3. The system of claim 2, wherein the one or more legal clause interpretations are identified in a plurality of attorney communications, and wherein the plurality of attorney communications comprises a plurality of email communications.
 4. The system of claim 3, wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting a redline change in a document attached to one of the plurality of email communications and identifying a paragraph associated with the redline change as a first legal clause interpretation of the one or more legal clause interpretations.
 5. The system of claim 3, wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting an addition in a document attached to one of the plurality of email communications and identifying a paragraph associated with the addition as a first legal clause interpretation of the one or more legal clause interpretations.
 6. The system of claim 3, wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting a comment in a document attached to one of the plurality of email communications and identifying text within the comment as a first legal clause interpretation of the one or more legal clause interpretations.
 7. The system of claim 1, wherein the instructions, when executed by the one or more processors, are further configured to cause the system to: receive, from a user device, reinforcement feedback based on the first interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.
 8. The system of claim 6, wherein the output of the first interpretation is in a chat program accessible by the user device and a reinforcement feedback is provided from the user device via the chat program.
 9. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: generate, via a trained neural network (NN), a first interpretation based on a first legal clause; receive reinforcement feedback based on the first interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.
 10. The system of claim 9, wherein the NN is either a convolutional neural network (CNN) or a recurrent neural network (RNN).
 11. The system of claim 10, wherein the plurality of attorney communications comprises a plurality of email communications, and the instructions, when executed by the one or more processors, are further configured to cause the system to: identify one or more legal clause interpretations in a plurality of attorney communications; and train the NN based on the identified one or more legal clause interpretations to form the trained NN.
 12. The system of claim 11, wherein identifying the one or more legal clause interpretations in the plurality of attorney communications comprises detecting a redline change in a document attached to one of the plurality of email communications and identifying a paragraph associated with the redline change as a first legal clause interpretation of the one or more legal clause interpretations.
 13. The system of claim 11, wherein identifying the one or more legal clause interpretation request in the plurality of attorney communications comprises detecting an addition in a document attached to one of the plurality of email communications and identifying a paragraph associated with the addition as a first legal clause interpretation of the one or more legal clause interpretations.
 14. The system of claim 11, wherein identifying the one or more legal clause interpretation request in the plurality of attorney communications comprises detecting a comment in a document attached to one of the plurality of email communications and identifying text within the comment as a first legal clause interpretation of the one or more legal clause interpretations.
 15. The system of claim 9, wherein the output of the first non-legalese interpretation is in a chat program accessible by a user device and the reinforcement feedback is provided from the user device via the chat program.
 16. A system, comprising: one or more processors; and a memory in communication with the one or more processors and storing instructions that, when executed by the one or more processors, are configured to cause the system to: generate, via a trained neural network (NN), a first interpretation based on a first legal clause; generate, using a probability model, a probability score based on a degree to which the first legal clause matches the non-legalese interpretation in meaning; determine whether the probability score exceeds a predetermined threshold; when the probability score does not exceed the predetermined threshold, instruct the NN to generate a second interpretation based on the first legal clause; and when the probability score exceeds the predetermined threshold, output the first interpretation.
 17. The system of claim 16, wherein the probability model is a convolutional neural network (CNN) and the neural network is at either a CNN or a recurrent neural network (RNN).
 18. The system of claim 16, wherein the instructions, when executed by the one or more processors, are further configured to cause the system to: receive reinforcement feedback based on the first interpretation; and iteratively re-train the trained NN based on the received reinforcement feedback.
 19. The system of claim 18, wherein the output of the first interpretation is in a chat program accessible by the user device and the reinforcement feedback is provided from the user device via the chat program.
 20. The system of claim 16, wherein the first interpretation comprises a first plain English interpretation. 